Bootstrap ARDL on Health Expenditure, CO2 Emissions, and GDP Growth Relationship for 18 OECD Countries
Bibliographic record
Abstract
Using annual time-series data over the period 1975–2017, the researcher apply the Bootstrap autoregressive distributed lag (ARDL) cointegration model developed by McNown et al.(2018), to examine whether there is a long run relationship among health expenditure, CO2 emissions (CO2), and gross domestic product (GDP) per capita in 18 OECD countries. We find cointegration exists in Netherlands when real GDP per capita serves as dependent variables, in New Zealand when health expenditure is the dependent variable and in the United States when CO2 emissions is dependent variable. The main results show evidence of a short run relationship between the three variables. The empirical results support that there is a bidirectional causality between health expenditure and GDP growth for Germany and the United States, between CO2 emissions and GDP growth for Canada, Germany, and the United States, and between health expenditure and CO2 emissions for New Zealand and Norway. The results also indicate that there are unidirectional causality in other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".